Concept

Data (computer science)

Related lectures (22)

Covers advanced functions of Pandas, focusing on filtering, labeling, and manipulating dataframes.

Covers generating code for a compiler, translating an Amy program to WebAssembly, including memory management and pattern matching compilation.

Covers the representation of structured data, arrays, structures, and functions with multiple return values.

Covers the representation of JSON data using case classes in Scala.

Offers a crash course in MATLAB, covering data representation, array indexing, mathematical operations, and common pitfalls.

Discusses data representation through models and systems, covering mathematical models, data structures, levels of modeling, and managing data.

Explores levels of data abstraction, model building, usage, and representation, and the utility of information systems for decision-making.

Covers binary code representation and the importance of programming languages for different tasks.

Explores the ROC curve, True Positive Rate, False Positive Rate, and prediction probabilities in classification models.

Explores data representation, databases, cloud computing, and challenges in the cloud environment.

Covers the introduction to arrays in Java, focusing on structured data representation and efficient data manipulation.

Delves into the concept of information starting from binary digits.

Explores data types in C/Arduino microcontroller programming, covering boolean, byte, char, word, arrays, and memory allocation.

Covers structured data representation, arrays, structures, variable declaration, initialization, and functions with multiple return values.

Explains the file system abstraction, focusing on files, directories, and their management within a computer system.

Explores the role of Computing in society and the basics of computing, algorithms, communication systems, and computer security.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Discusses data visualization techniques, emphasizing their applications and principles for effective communication of complex information.

Covers data representations and processing techniques essential for effective machine learning algorithms.